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Create lbw_detector.py

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  1. lbw_detector.py +76 -0
lbw_detector.py ADDED
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+ from ultralytics import YOLO
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+ import cv2
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+ import numpy as np
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+ import os
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+
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+ # Load YOLO model (custom-trained or pretrained with compatible classes)
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+ model = YOLO("yolov8n.pt") # Replace with "lbw_yolov8.pt" if custom-trained
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+
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+ # Target class IDs — update based on your custom model class mapping
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+ CLASS_NAMES = {
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+ 0: "ball",
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+ 1: "bat",
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+ 2: "pad",
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+ 3: "stump",
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+ 4: "player"
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+ }
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+
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+ def detect_lbw_event(frames):
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+ """
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+ Detects ball, bat, stump, and pad in each frame.
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+ Identifies impact and prepares coordinates for trajectory modeling.
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+
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+ Returns:
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+ dict: {
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+ "ball_positions": [x, y] list per frame,
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+ "impact_frame": int,
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+ "impact_type": str,
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+ "objects_per_frame": [
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+ {"ball": (x, y), "pad": (x, y), ...}
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+ ]
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+ }
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+ """
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+ ball_positions = []
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+ impact_frame = -1
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+ impact_type = None
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+ objects_per_frame = []
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+
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+ for idx, frame in enumerate(frames):
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+ results = model(frame)[0]
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+ frame_objects = {}
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+
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+ for det in results.boxes.data:
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+ x1, y1, x2, y2, conf, cls = det.cpu().numpy()
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+ class_id = int(cls)
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+ class_name = CLASS_NAMES.get(class_id, "unknown")
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+ center_x = int((x1 + x2) / 2)
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+ center_y = int((y1 + y2) / 2)
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+ frame_objects[class_name] = (center_x, center_y)
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+
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+ if class_name == "ball":
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+ ball_positions.append((idx, center_x, center_y))
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+
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+ objects_per_frame.append(frame_objects)
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+
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+ # Basic impact logic: ball overlaps pad or bat
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+ if "ball" in frame_objects and ("pad" in frame_objects or "bat" in frame_objects):
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+ bx, by = frame_objects["ball"]
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+ if "pad" in frame_objects:
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+ px, py = frame_objects["pad"]
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+ if abs(bx - px) < 30 and abs(by - py) < 30:
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+ impact_frame = idx
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+ impact_type = "pad"
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+ break
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+ if "bat" in frame_objects:
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+ tx, ty = frame_objects["bat"]
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+ if abs(bx - tx) < 30 and abs(by - ty) < 30:
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+ impact_frame = idx
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+ impact_type = "bat"
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+ break
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+
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+ return {
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+ "ball_positions": ball_positions,
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+ "impact_frame": impact_frame,
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+ "impact_type": impact_type,
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+ "objects_per_frame": objects_per_frame
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+ }